Response surface methodology
response surface methodology, response surface methodology pdfIn statistics, response surface methodology RSM explores the relationships between several explanatory variables and one or more response variables The method was introduced by George E P Box and K B Wilson in 1951 The main idea of RSM is to use a sequence of designed experiments to obtain an optimal response Box and Wilson suggest using a seconddegree polynomial model to do this They acknowledge that this model is only an approximation, but they use it because such a model is easy to estimate and apply, even when little is known about the process
Statistical approaches such as RSM can be employed to maximize the production of a special substance by optimization of operational factors In contrast to conventional methods, the interaction among process variables can be determined by statistical techniques
Contents
 1 Basic approach of response surface methodology
 2 Important RSM properties and features
 3 Special geometries
 31 Cube
 32 Sphere
 33 Simplex geometry and mixture experiments
 4 Extensions
 41 Multiple objective functions
 5 Practical concerns
 6 See also
 7 References
 71 Historical
 8 External links
Basic approach of response surface methodology
An easy way to estimate a firstdegree polynomial model is to use a factorial experiment or a fractional factorial design This is sufficient to determine which explanatory variables affect the response variables of interest Once it is suspected that only significant explanatory variables are left, then a more complicated design, such as a central composite design can be implemented to estimate a seconddegree polynomial model, which is still only an approximation at best However, the seconddegree model can be used to optimize maximize, minimize, or attain a specific target for
Important RSM properties and features
RESPONSE SURFACE OPTIMIZATION USING JMP SOFTWARE
ORTHOGONALITY: The property that allows individual effects of the kfactors to be estimated independently without or with minimal confounding Also orthogonality provides minimum variance estimates of the model coefficient so that they are uncorrelated
ROTATABILITY: The property of rotating points of the design about the center of the factor space The moments of the distribution of the design points are constant
UNIFORMITY: A third property of CCD designs used to control the number of center points is uniform precision or Uniformity
Special geometries
Cube
Cubic designs are discussed by Kiefer, by Atkinson, Donev, and Tobias and by Hardin and Sloane
Sphere
Spherical designs are discussed by Kiefer and by Hardin and Sloane
Simplex geometry and mixture experiments
Mixture experiments are discussed in many books on the design of experiments, and in the responsesurface methodology textbooks of Box and Draper and of Atkinson, Donev and Tobias An extensive discussion and survey appears in the advanced textbook by John Cornell
Extensions
Multiple objective functions
See also: Multiobjective optimization and Pareto efficiencySome extensions of response surface methodology deal with the multiple response problem Multiple response variables create difficulty because what is optimal for one response may not be optimal for other responses Other extensions are used to reduce variability in a single response while targeting a specific value, or attaining a near maximum or minimum while preventing variability in that response from getting too large
Practical concerns
Response surface methodology uses statistical models, and therefore practitioners need to be aware that even the best statistical model is an approximation to reality In practice, both the models and the parameter values are unknown, and subject to uncertainty on top of ignorance Of course, an estimated optimum point need not be optimum in reality, because of the errors of the estimates and of the inadequacies of the model
Nonetheless, response surface methodology has an effective trackrecord of helping researchers improve products and services: For example, Box's original responsesurface modeling enabled chemical engineers to improve a process that had been stuck at a saddlepoint for years The engineers had not been able to afford to fit a cubic threelevel design to estimate a quadratic model, and their biased linearmodels estimated the gradient to be zero Box's design reduced the costs of experimentation so that a quadratic model could be fit, which led to a longsought ascent direction
See also
 Plackett–Burman design
 Box–Behnken design
 Central composite design
 IOSO method based on responsesurface methodology
 Optimal designs
 Polynomial regression
 Polynomial and rational function modeling
 Surrogate model
 Probabilistic design
 GradientEnhanced Kriging GEK
References
 ^ Asadi, Nooshin; Zilouei, Hamid March 2017 "Optimization of organosolv pretreatment of rice straw for enhanced biohydrogen production using Enterobacter aerogenes" Bioresource Technology 227: 335–344 doi:101016/jbiortech201612073
 ^ Ahmed Maged, Salah Haridy, Mohammad Shamsuzzaman, Imad Alsyouf, and Roubi Zaied 2018Statistical Monitoring and Optimization of Electrochemical Machining using Shewhart Charts and Response Surface Methodology32, 6877 https://doiorg/1026776/ijemm0302201801
 ^ Box, G E P and Wilson, KB 1951 On the Experimental Attainment of Optimum Conditions with discussion Journal of the Royal Statistical Society Series B131:1–45
 ^ Improving Almost Anything: Ideas and Essays, Revised Edition Wiley Series in Probability and Statistics George E P Box
 ^ Soltani, M and Soltani, J 2016 Determination of optimal combination of applied water and nitrogen for potato yield using response surface methodology RSM Journal of Bioscience Biotechnology Research Communication 91: 4654 Online Contents Available at: http://wwwbbrcin
 Box, G E P and Wilson, KB 1951 On the Experimental Attainment of Optimum Conditions with discussion Journal of the Royal Statistical Society Series B 131:1–45
 Box, G E P and Draper, Norman 2007 Response Surfaces, Mixtures, and Ridge Analyses, Second Edition , Wiley
 Atkinson, A C and Donev, A N and Tobias, R D 2007 Optimum Experimental Designs, with SAS Oxford University Press pp 511+xvi ISBN 9780199296606 External link in publisher= helpCS1 maint: Multiple names: authors list link
 Cornell, John 2002 Experiments with Mixtures: Designs, Models, and the Analysis of Mixture Data third ed Wiley ISBN 0471079162
 Goos, Peter 2002 The Optimal Design of Blocked and Splitplot Experiments Lecture Notes in Statistics 164 Springer External link in publisher= help
 Kiefer, Jack Carl 1985 L D Brown; et al, eds Jack Carl Kiefer Collected Papers III Design of Experiments SpringerVerlag ISBN 038796004X
 Pukelsheim, Friedrich 2006 Optimal Design of Experiments SIAM ISBN 9780898716047 External link in publisher= help
 R H Hardin and N J A Sloane, "A New Approach to the Construction of Optimal Designs", Journal of Statistical Planning and Inference, vol 37, 1993, pp 339369
 R H Hardin and N J A Sloane, "ComputerGenerated Minimal and Larger Response Surface Designs: I The Sphere"
 R H Hardin and N J A Sloane, "ComputerGenerated Minimal and Larger Response Surface Designs: II The Cube"
 Ghosh, S; Rao, C R, eds 1996 Design and Analysis of Experiments Handbook of Statistics 13 NorthHolland ISBN 0444820612
 Draper, Norman & Lin, Dennis K J "Response Surface Designs" pp 343–375 Missing or empty title= help
 Gaffke, N & Heiligers, B 1996 "Polynomial Regression" Handbook of Statistics, Volume 13 Design and Analysis of Experiments pp 1149–1199 doi:101016/S0169716196130327
Historical
 Gergonne, J D 1974 "The application of the method of least squares to the interpolation of sequences" Historia Mathematica Translated by Ralph St John and S M Stigler from the 1815 French ed 1 4: 439–447 doi:101016/0315086074900342
 Stigler, Stephen M 1974 "Gergonne's 1815 paper on the design and analysis of polynomial regression experiments" Historia Mathematica 1 4: 431–439 doi:101016/0315086074900330
 Peirce, C S 1876 "Note on the Theory of the Economy of Research" Coast Survey Report: 197–201 Appendix No 14 NOAA PDF Eprint Reprinted in Collected Papers of Charles Sanders Peirce 7 1958 paragraphs 139–157, and in Peirce, C S July–August 1967 "Note on the Theory of the Economy of Research" Operations Research 15 4: 643–648 doi:101287/opre154643 Abstract at JSTOR
 Smith, Kirstine 1918 "On the Standard Deviations of Adjusted and Interpolated Values of an Observed Polynomial Function and its Constants and the Guidance They Give Towards a Proper Choice of the Distribution of the Observations" Biometrika 12 1/2: 1–85 doi:102307/2331929 JSTOR 2331929
External links
 Response surface designs
 Response surface analysis



Scientific method 

Treatment and blocking 

Models and inference 

Designs Completely randomized 





Computational statistics 


Correlation and dependence 


Regression analysis 


Regression as a statistical model 


Decomposition of variance 


Model exploration 


Background 


Design of experiments 


Numerical approximation 


Applications 



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